My team is migrating to a shared multi-node cluster and we are struggling with job prioritization. Could someone explain the key differences between the Capacity Scheduler and the Fair Scheduler in YARN? We need to ensure our critical Spark jobs get the resources they need while preventing lower-priority MapReduce tasks from starving the system in a production environment.
3 answers
In a multi-tenant environment, the choice depends on your organizational structure. The Capacity Scheduler is designed for large shared clusters where you want to guarantee a minimum percentage of the cluster to specific departments or "queues." It’s very predictable. The Fair Scheduler, on the other hand, aims to give all apps an equal share of resources over time. If a high-priority job arrives, it can preempt resources from others. For critical Spark jobs, I recommend using the Capacity Scheduler with "priority" enabled, allowing you to move urgent jobs to the front of the queue without completely starving others.
How are you managing your container memory limits? If your Spark jobs are frequently hitting the YARN memory overhead limit, it might look like a scheduling issue when it's actually a configuration one.
We switched to the Capacity Scheduler with hierarchical queues. It allowed our Finance and Analytics teams to share a cluster without the Finance reports getting delayed by R&D experiments.
Sarah is right; hierarchical queues are the way to go for enterprise setups. It provides much better governance than a flat scheduling structure.
David, that’s a sharp observation. We recently adjusted our yarn.nodemanager.resource.memory-mb settings and it helped. However, we still see instances where a single heavy MapReduce job locks up the queue. We are now looking into implementing "Preemption" in our Fair Scheduler configuration to allow our low-latency Spark jobs to "kick out" long-running batch processes when resources are tight.